AI Research Today unpacks the latest advancements in artificial intelligence, one paper at a time. We go beyond abstracts and headlines, walking through architectures, experiments, training details, ablations, failure modes, and the implications for future work. Each episode will choose between one and three new, impactful research papers and go through them in depth. We will discuss the papers at the level of an industry practitioner or AI researcher. If you want to understand the newest topics in AI research but don't have the time to dig through the papers yourself, this is your solution.
How Does a Diffusion Model Work, Part 2: Flow Matching and Diffusion
Send us Fan Mail Diffusion models have become the foundation of modern generative AI, powering state-of-the-art systems for image generation, video synthesis, protein design, and more. In the first episode of this series, we introduced generative modeling as a sampling problem and developed the probabilistic framework needed to think about how models learn complex data distributions. In this second episode, we move into the core mathematics behind flow matching and diffusion modeling, continu...
2 Jul 2026
How Does a Diffusion Model Work, Part 1: Introduction
Send us Fan Mail Diffusion models have become the foundation of modern generative AI, powering state-of-the-art systems for image generation, video synthesis, protein design, and more. But behind the impressive demos lies a surprisingly elegant mathematical framework. In this first episode of a multi-part series, we begin working through the excellent MIT lecture notes An Introduction to Flow Matching and Diffusion Models by Peter Holderrieth and Ezra Erives. Rather than jumping straight into...
3 Jun 2026
Generative Recursive Reasoning
Send us Fan Mail In this episode, we explore the paper "Generative Recursive Reasoning (GRAM)," a fascinating new approach to AI reasoning co-authored by Yoshua Bengio and researchers from Mila and Samsung AI. Most modern AI systems reason by generating more tokens. GRAM takes a different approach: instead of extending a chain of thought, it repeatedly refines an internal latent state. The key innovation is introducing probabilistic reasoning trajectories, allowing the model to explore multip...
12 May 2026
OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language Environment Simulation
Send us Fan Mail In this episode, we break down the new paper “OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language Environment Simulation,” which explores how AI agents can be benchmarked across real occupational domains like healthcare, logistics, manufacturing, customs processing, and more. The paper introduces OccuBench, a large-scale benchmark spanning 100 professional task scenarios across 65 specialized domains. One of the most interesting ideas is the use of L...
23 Apr 2026
GradMem: Teaching LLMs to Remember (Without Retraining)
Send us Fan Mail In this episode, we break down GradMem, a new approach to memory in large language models: https://arxiv.org/pdf/2603.13875v1 Instead of relying on the transformer KV cache or repeatedly reprocessing documents (like in RAG), GradMem introduces a different idea—learn a compact memory representation at inference time. Using a few steps of gradient descent, the model “writes” important information from a context into a small set of memory tokens, allowing it to answer future ...
23 Mar 2026
Language Models are Injective and Hence Invertible
Send us Fan Mail In this episode, we break down a fascinating new result from recent research: that modern Transformer language models are almost surely injective—meaning different prompts map to unique internal representations, with no information loss. We dig into the paper: Read the paper on arXiv At the core of the proof is a surprisingly deep mathematical idea: Transformers are real analytic functions of their parameters, which allows researchers to rigorously reason about when “collis...
16 Feb 2026
Learning to Reason in 13 Parameters
Send us Fan Mail Link to arxiv: https://arxiv.org/pdf/2602.04118 Large language models have recently shown impressive reasoning abilities, often learned through reinforcement learning and low-rank adaptation techniques like LoRA. But these approaches still assume that effective reasoning requires relatively large adaptation layers. This new paper challenges that assumption by asking a provocative question: how small can a reasoning update really be? In this episode, we explore Learning t...
26 Jan 2026
SPIRAL: Symbolic LLM Planning via Grounded and Reflective Search
Send us Fan Mail Large Language Models often struggle with complex planning tasks that require exploration, backtracking, and self-correction. Once an LLM commits to an early mistake, its linear chain-of-thought reasoning makes recovery difficult. While search methods like Monte Carlo Tree Search (MCTS) offer a way to explore alternatives, they typically rely on sparse rewards and fail to fully exploit the semantic strengths of language models. In this episode, we dive into SPIRAL (Symbolic L...
12 Jan 2026
Meta-RL Induces Exploration In Language Agents
Send us Fan Mail Episode Paper: https://arxiv.org/pdf/2512.16848 In this episode, we dive into a cutting-edge AI research breakthrough that tackles one of the biggest challenges in training intelligent agents: how to explore effectively. Standard reinforcement learning (RL) methods help language model agents learn to interact with environments and solve multi-step tasks, but they often struggle when the tasks require active exploration—that is, learning what to try next when the best s...
29 Dec 2025
DeepSearch: Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search
Send us Fan Mail In this episode, we unpack DeepSearch, a new paradigm in reinforcement learning with verifiable rewards (RLVR) that aims to overcome one of the biggest bottlenecks in training reasoning-capable AI systems. Traditional reinforcement learning methods often plateau after extensive training because they rely on sparse exploration and limited rollouts, leaving critical reasoning paths undiscovered and unlearned. DeepSearch turns this model training approach on its head by embeddin...
11 Dec 2025
Transformer-Squared: Self-Adaptive LLMs
Send us Fan Mail In this episode we’re diving into “Transformer-Squared: Self-Adaptive LLMs” — a new framework for adapting large language models to unseen tasks on the fly by tuning only a small part of their weights. The central idea is Singular Value Fine-Tuning (SVF), a parameter-efficient fine-tuning technique that decomposes each weight matrix with Singular Value Decomposition (SVD) and then only trains a small vector that scales the singular values. These vectors become compact “expert...
1 Dec 2025
Nested Learning: The Illusion of Deep Learning Architectures
Send us Fan Mail NL.pdf In this episode, we dive into Nested Learning (NL) — a new framework that rethinks how neural networks learn, store information, and even modify themselves. While modern language models have made remarkable progress, fundamental questions remain: How do they truly memorize? How do they improve over time? And why does in-context learning emerge at scale? Nested Learning proposes a bold answer. Instead of viewing a model as a single optimization problem, NL treats ...
24 Nov 2025
AgentEvolver: An Autonomous Agent Framework
Send us Fan Mail https://arxiv.org/pdf/2511.10395 What if AI agents could teach themselves? In this episode, we dive into AgentEvolver, a groundbreaking framework from Alibaba's Tongyi Lab that flips the script on how we train autonomous AI agents. Traditional agent training is brutal: you need manually crafted datasets, expensive random exploration, and mountains of compute. AgentEvolver introduces a self-evolving system with three elegant mechanisms that let the LLM drive its own learning: ...
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